To address the above limitations and deliver detection accuracy (and generalization), data-driven techniques using deep learning models have seen significant traction in recent years. These models typically consist of neural networks that determine whether or not a given binary is malicious or benign based on various, defining features of that binary. For instance, certain models run inference over PE header values, assembly code, network traffic, and even the names of binaries [1]}, [2]}. Others follow a dynamic approach and perform manual feature engineering of API calls [3]}. Most recently, the MalConv CNN [4]} performs malware detection by operating directly over the raw bytes in a binary, thereby eschewing labor-intensive feature engineering and the need for domain expertise.
